On the usability of deep networks for object-based image analysis

Nicolas Audebert 1, 2 Bertrand Le Saux 2 Sébastien Lefèvre 1
1 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : As computer vision before, remote sensing has been radically changed by the introduction of Convolution Neural Networks. Land cover use, object detection and scene understanding in aerial images rely more and more on deep learning to achieve new state-of-the-art results. Recent architectures such as Fully Convolutional Networks (Long et al., 2015) can even produce pixel level annotations for semantic mapping. In this work, we show how to use such deep networks to detect, segment and classify different varieties of wheeled vehicles in aerial images from the ISPRS Potsdam dataset. This allows us to tackle object detection and classification on a complex dataset made up of visually similar classes, and to demonstrate the relevance of such a subclass modeling approach. Especially, we want to show that deep learning is also suitable for object-oriented analysis of Earth Observation data. First, we train a FCN variant on the ISPRS Potsdam dataset and show how the learnt semantic maps can be used to extract precise segmentation of vehicles, which allow us studying the repartition of vehicles in the city. Second, we train a CNN to perform vehicle classification on the VEDAI (Razakarivony and Jurie, 2016) dataset, and transfer its knowledge to classify candidate segmented vehicles on the Potsdam dataset.
Type de document :
Communication dans un congrès
International Conference on Geographic Object-Based Image Analysis (GEOBIA), Sep 2016, Enschede, Netherlands. 〈https://www.geobia2016.com/〉
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Soumis le : mardi 20 septembre 2016 - 18:26:31
Dernière modification le : mercredi 2 août 2017 - 10:09:31

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  • HAL Id : hal-01320010, version 1
  • ARXIV : 1609.06845

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Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre. On the usability of deep networks for object-based image analysis. International Conference on Geographic Object-Based Image Analysis (GEOBIA), Sep 2016, Enschede, Netherlands. 〈https://www.geobia2016.com/〉. 〈hal-01320010〉

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